Geometric and potential driving formation and evolution of biomolecular surfaces.
This paper presents new geometrical flow equations for the theoretical modeling of biomolecular surfaces in the context of multiscale implicit solvent models. To account for the local variations near the biomolecular surfaces due to interactions between solvent molecules, and between solvent and solute molecules, we propose potential driven geometric flows, which balance the intrinsic geometric forces that would occur for a surface separating two homogeneous materials with the potential forces induced by the atomic interactions. Stochastic geometric flows are introduced to account for the random fluctuation and dissipation in density and pressure near the solvent-solute interface. Physical properties, such as free energy minimization (area decreasing) and incompressibility (volume preserving), are realized by some of our geometric flow equations. The proposed approach for geometric and potential forces driving the formation and evolution of biological surfaces is illustrated by extensive numerical experiments and compared with established minimal molecular surfaces and molecular surfaces. Local modification of biomolecular surfaces is demonstrated with potential driven geometric flows. High order geometric flows are also considered and tested in the present work for surface generation. Biomolecular surfaces generated by these approaches are typically free of geometric singularities. As the speed of surface generation is crucial to implicit solvent model based molecular dynamics, four numerical algorithms, a semi-implicit scheme, a Crank-Nicolson scheme, and two alternating direction implicit (ADI) schemes, are constructed and tested. Being either stable or conditionally stable but admitting a large critical time step size, these schemes overcome the stability constraint of the earlier forward Euler scheme. Aided with the Thomas algorithm, one of the ADI schemes is found to be very efficient as it balances the speed and accuracy.
Bates
P W
PW
Department of Mathematics, Michigan State University, East Lansing, MI 48824, USA. bates@math.msu.edu
Chen
Zhan
Z
Sun
Yuhui
Y
Wei
Guo-Wei
GW
Zhao
Shan
S
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D000818:Animals; D001059:Apoproteins / Q000737:chemistry; D001426:Bacterial Proteins; D003198:Computer Simulation; D029968:Escherichia coli Proteins / Q000737:chemistry; D006801:Humans; D053605:Leukemia Inhibitory Factor / Q000737:chemistry; D008958:Models, Molecular; D009498:Neurotoxins / Q000737:chemistry; D009687:Nuclear Proteins / Q000737:chemistry; D009696:Nucleic Acids / Q000737:chemistry*; D010940:Plant Proteins / Q000737:chemistry; D000071156:Promyelocytic Leukemia Protein; D011506:Proteins / Q000737:chemistry*; D011973:Receptors, LDL / Q000737:chemistry; D012097:Repressor Proteins / Q000737:chemistry; D012234:Rho Factor / Q000737:chemistry; D012997:Solvents / Q000737:chemistry; D055672:Static Electricity; D013269:Stochastic Processes; D013499:Surface Properties; D013816:Thermodynamics; D014157:Transcription Factors / Q000737:chemistry; D025521:Tumor Suppressor Proteins / Q000737:chemistry
D000596:Amino Acids; D001059:Apoproteins; D001426:Bacterial Proteins; D029968:Escherichia coli Proteins; D053605:Leukemia Inhibitory Factor; D009498:Neurotoxins; D009687:Nuclear Proteins; D009696:Nucleic Acids; D010940:Plant Proteins; D000071156:Promyelocytic Leukemia Protein; D011506:Proteins; D011973:Receptors, LDL; D012097:Repressor Proteins; D012234:Rho Factor; D012997:Solvents; C027190:TRPR protein, E coli; D014157:Transcription Factors; D025521:Tumor Suppressor Proteins; C070576:PML protein, human; C024934:crambin protein, Crambe abyssinica